Predictive power of word surprisal for reading times is a linear function of language model quality

Transcription

1 Predictive power of word surprisal for reading times is a linear function of language model quality Adam Goodkind & Klinton Bicknell Northwestern University Cognitive Modeling & Computational Linguistics Workshop

2 PROBABILITY IN CONTEXT 2 Don t touch the wet paint cement bed (Wlotko & Federmeier, 2012)

3 MOTIVATION HOW WE USE PROBABILITY IN CONTEXT 3 Studies of human sentence processing have shown that a word s probability in context is strongly related to processing difficulty Do better estimates of word probability improve processing predictions? ERP Response Reading Times (Wlotko & Federmeier, 2012) (Hale, 2001)

4 SURPRISAL AND SURPRISAL THEORY 4 From information theory (Shannon, 1948) A theory of communication The information content in a word = -log(p) More information is more difficult to process Difficulty (cognitive cost of processing a word) how predictable the word is in a given context (Hale, 2001; Levy, 2008) Prior studies (e.g. Demberg & Keller, 2008) found that surprisal can predict reading times

5 LANGUAGE MODELS CALCULATING WORD PROBABILITIES 5 Cloze task (Taylor, 1953) Count people's responses to filling in a missing word Inaccurate and labor intensive à need for computational models Language models A probability distribution over sequences of words Good language models assign a higher probability to word strings that occur more often Quality (accuracy) of a language model is quantified as perplexity Lower == Better

6 MANY TYPES OF LANGUAGE MODELS DIFFERENT BUILDING BLOCKS 6 n-grams (fixed sequence length) Bigrams, trigrams, 4-grams, etc. p(w n w n-1 ) Fixed dependency length Neural network Word probabilities use dependencies spanning arbitrary distances (number of words) Usually use Long Short-Term Memory (LSTM) networks Variable dependency length Interpolated Combine multiple models Recent neural network-based language models have significantly improved linguistic accuracy n-grams Prior Work NN interpolated

7 DEFINING ACCURACY 7 Linguistic accuracy How well language models predict unseen language Measured by perplexity Psychological accuracy How well language models predict psychological phenomena E.g. eye gaze duration, ERP response amplitude

8 OUR STUDY 8 Build a range of different types of language models Different language models produce different estimates of surprisal Construct a regression model predicting gaze duration in an eye-tracking corpus from the surprisal of each language model Compare the regression models quality of predictions for the gaze durations Understand the relationship between language model quality and predictions of processing difficulty

9 METHODS CREATING A LANGUAGE MODEL 9 Language models used Google One Billion Word Benchmark ( 1b ) Corpus Collected from international English news services ~900 million words, 800,000 word vocabulary size n-grams models created with kenlm Kneser-Ney smoothing Neural network model created from Google s pre-trained models Long Short-Term Memory (LSTM) units in a Recurrent Neural Network (RNN) Interpolated models created by mixing LSTM and 5-gram estimates

10 OUR LANGUAGE MODELS 10 n-grams NN interpolated

11 METHODS EYE-TRACKING DATA 11 Dundee Corpus 61,000 tokens from a British newspaper, read by 10 participants ~300,000 total tokens, 37,000 word vocabulary size Extracted gaze durations: how long a word was fixated during first pass reading Exclusions Words not fixated Words at beginning/end of line and others

12 METHODS PREDICTIVE REGRESSION MODELS 12 Generalized Additive Models (GAMs) Type of regression model Allows for non-linear effects Predictors of interest Surprisal of current and previous words

13 METHODS PREDICTIVE REGRESSION MODELS 13 We used Generalized Additive Mixed Models (GAMMs) Predict eye gaze duration given: Surprisal of current and previous word Non-linear effects of control covariates The interaction of word frequency and length Sequential word number Whether the prior word was fixated Random intercepts for each subject

14 METHODS PREDICTIVE REGRESSION MODELS 14 Linear versus non-linear GAMMs First set of experiments forced surprisal to be a linear predictor Second set of experiments allowed surprisal to make non-linear predictions Other predictors remained non-linear

15 METHODS PSYCHOLOGICAL ACCURACY 15 Measured improvements in predictions from each language model ΔLogLik(model m ) = LogLik(model m ) LogLik(baseline_model) LogLik (Log Likelihood) A measure of accuracy model m Includes language model m s surprisal as a predictor baseline_model Missing predictor of interest (surprisal) Includes only control covariates

16 RESULTS RELATIONSHIP BETWEEN LINGUISTIC AND PSYCHOLOGICAL ACCURACY 16 Using a linear regression model, we investigate the relationship between language models and their psychological predictions What is the relationship between linguistic accuracy (perplexity) and psychological prediction quality (ΔLogLik)?

17 RESULTS RELATIONSHIP BETWEEN LINGUISTIC AND PSYCHOLOGICAL ACCURACY 17 As the perplexity of a language model improves, the model makes more accurate predictions for reading times Linear GAMMs This relationship holds across model types

18 RESULTS MAGNITUDE OF EFFECT 18 As language models continue to improve and make better predictions, does the magnitude (size of effect) of surprisal change? Do better language models put more weight on the surprisal of current and previous words? We can compare coefficients of surprisal from each model to understand the magnitude of the effect

19 RESULTS MAGNITUDE OF EFFECT 19 The magnitude of the effect does not correlate with linguistic accuracy Effect size of surprisal does not seem to be biased for worse language models Current word Previous word

20 RESULTS SHAPE OF EFFECT 20 Smith & Levy (2013) looked at the shape of the effect of surprisal Found a linear relationship Supports various derivations of surprisal theory (e.g., Hale, 2001; Levy, 2008; Bicknell & Levy, 2009; Smith & Levy, 2013) Contra alternative probabilistic processing theories (e.g., Narayanan & Jurafsky, 2004; theories predicting UID optimality) Does this linear relationship hold for more sophisticated models, if we allow surprisal to be non-linear?

21 RESULTS SHAPE OF EFFECT 21 For both the current and previous word probability, gaze time changes at a linear rate, for all models Possibly even more linear as language model accuracy improves Current word Previous word

22 RESULTS RELATIONSHIP BETWEEN LINGUISTIC AND PSYCHOLOGICAL ACCURACY (PART II) 22 If we allow for non-linear effects, not only does the relationship between models improve, but the relationship becomes more linear Non-linear GAMMs

23 TAKEAWAYS 23 Strong relationship between linguistic model quality and its psychological predictive power No privileged language model class: better perplexity improves psychological predictions The size of the surprisal effect was consistent across models Estimates of the effect size of surprisal from worse language models appear to be relatively unbiased The effect of surprisal is linear across all models and distributions of word probabilities Supports surprisal theory processing models even with state-of-the-art language models

24 THANK YOU! Funding sources: Adam Goodkind